Computational screening of MOFs for CO2 capture

Abstract

The capture of CO2 (carbon dioxide) is an urgent environmental issue due to global warming. Adsorption-based CO2 capture using a new family of porous materials, metal-organic frameworks (MOFs), has been considered as a promising alternative to conventional methods. The rapid increase in the number of synthesized MOFs offers various materials for efficient CO2 capture, but assessing the performance of each MOF material using purely experimental methods is challenging. Recent progress in computational tools, high-throughput molecular simulations, and machine learning algorithms provide great opportunities for effective computational screening of MOFs with the aim of identifying the most promising adsorbents for CO2 capture prior to experimental studies. In this chapter, we focused on the recent advances in high-throughput screening of MOFs for CO2 capture and separation. We first reviewed the details of molecular simulation methods to compute CO2 adsorption properties of MOFs and adsorbent performance evaluation metrics that have been used to assess the CO2 separation potential of MOFs. Large-scale computational screening studies and quantitative structure-performance relationships obtained from molecular simulations were then discussed. Finally, opportunities and challenges of using computational tools to reveal the potential of MOFs for CO2 capture and separation were addressed.